N. Sánchez-Gómez, Jesús Torres Valderrama, J. A. García-García, Javier Gutiérrez · 5 authors
Blockchain technology promises to spark a real revolution. One of most important concepts associated with this technology is smart contracts, which enable the automatic execution of agreements and augur a world without intermediaries. The conditions and rules of “contracts” are established in a computer codes and trust is enforced by consensus among the participants. One relevant feature associated with smart contract is the immutability property, which establishes the non-alteration of blockchain network data after the clauses of the contract are been approved by all parties or entities involved. For this reason, smart contract development requires more effort and care than the development of other common programs. They require systematic mechanisms to collect requirements and functional specifications. In addition, it is necessary to verify and validate the agreed functionality and the implemented code before they are deployed in the blockchain platform. This article presents a systematic literature review of primary studies in the field of Software Development Life Cycle, focusing on model-based software design and testing in the blockchain domain of smart contracts. This research aims to identify gaps and/or opportunities for further research. After carried out this review, it was observed that no clear methodology exists for evaluating and validating the quality either of this software or the overall development process. This means that software developers may implement smart contract code in which bugs and serious security vulnerabilities appear when the software is delivered to their customers.
Mazin Debe, Khaled Salah, Muhammad Habib ur Rehman, Davor Svetinović
Public fog nodes can be deployed in public places closer to the edge where many personal and commercial devices (e.g., a sensor, an application, or a device) can connect to. These public fog nodes can provide real-time and localized services for networking, computing, storage and content delivery to the connected devices. The monetization and payment of such services is typically manual, centralized, and lacks the necessary trust. The providers of the public fog nodes typically offer fixed pricing models for their services, and the customers manually select and pay for the used services, with little or no transparency and trust in the provided service in terms of the used time, network bandwidth, and quality of service (QoS). This paper presents a novel scheme to enable blockchain-based monetization and automated payment in cryptocurrency for services provided by public fog nodes. The proposed scheme is decentralized, trustworthy, automated, and with certain guarantees for QoS, customer satisfaction, and dispute resolutions through a reputation system. The proposed solution uses the Ethereum blockchain and its native smart contract features to govern the interactions between devices and fog nodes. The proposed solution is implemented, tested and evaluated to show correct behavior and functionality. We also provide cost and security analysis and show that our solution is resilient against major security attacks. Our smart contract is made publicly available on Github1.
Abstract By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
Tommy Crépellière, Matthias Pelster, Stefan Zeisberger
Arbitrage opportunities in markets for cryptocurrencies are well-documented. In this paper, we confirm that they existed; however, their magnitude decreased greatly from April 2018 onward. Analyzing various trading strategies, we show that it is hardly possible to exploit existing price differences since then. We discuss and test several mechanisms that may be responsible for the increased market efficiency and find that, particularly, informed trading is correlated with a reduction in arbitrage opportunities.
Cryptocurrencies have been a target for cybercriminal activities because of the pseudo-anonymity and privacy they offer. Researchers have been actively working on analyzing and developing innovative defensive mechanisms to prevent these activities. A significant challenge facing researchers is collecting datasets to train defensive systems to detect and analyze these cyberattacks. Our aims in this systematic review are to explore and aggregate the state of the art threats that have emerged with cryptocurrencies and the defensive mechanisms that have been proposed. We also discuss the threats type, scale, and how efficient the defensive mechanisms are in providing early detection and prevention. We also list out the resources that have been used to collect datasets, and we identify the publicly available ones. In this study, we extracted 1,221 articles from four top scientific and engineering databases and libraries in Computer Science: IEEE Xplore, ACM Digital Library, Elsevier's Scopus, and Crarivate's Web of Science. We defined inclusion, exclusion, and quality of assessment criteria, and after a detailed review process, 66 publications were included in the final review. Our analysis revealed that the literature contains a significant amount of research to detect and analyze several attack types, such as the high yield investment programs and pump and dump. These attacks have been used to steal millions of USD, abuse millions of connected devices, and have created even more significant loss in denial of services and productivity losses. We have found that the researchers use various sources to collect training datasets. Many authors have made their dataset publicly available. We have created a list of these datasets, which we have made available along with other supplementary websites, tools, and libraries that can be used in the data collection and analysis process.
Lasse Herskind, Panagiota Katsikouli, Nicola Dragoni
Our transaction history in the current centralized banking system has the ability to reveal a lot of private information for each spender, both to the banking system itself, but also to those entities that surround it (e.g., governments, industry etc). Examples of leaking information constitute the amounts spent, the goods on which the amounts were spent, the spending locations and the users we exchange money with. This knowledge is powerful in the hands of those who have it, and can be used in multiple ways, not always to our benefit. Cryptocurrencies, such as the famous Bitcoin, were proposed as a means to address the limitations of centralized banking systems and to offer its users privacy with regards to their transactional data. In this work, we perform a systematic literature review on the realm of privacy for electronic currencies. We present the development of digital money from electronic cash to cryptocurrencies and focus on the techniques that are employed to enhance user-privacy. Furthermore, we present flaws of the current cryptocurrency systems, which reduce the privacy of the cryptocurrency users. Finally, we describe three research directions to enhance privacy for cryptocurrencies: transaction propagation mechanisms, succinct ZK proof systems without a trusted setup, and specialised trustless zero-knowledge proofs.
Mauro C. Argañaraz, Mario Berón, Maria João Varanda Pereira, Pedro Rangel Henriques
Ethereum is the principal ecosystem based on blockchain that provides a suitable environment for coding and executing smart contracts, which have been receiving great attention due to the commercial apps and among the scientific community. The process of writing secure and well performing contracts in the Ethereum platform is a major challenge for developers. It consists of the application of non-conventional programming paradigms due to the inherent characteristics of the execution of distributed computing programs. Furthermore, the errors in the deployed contracts could have serious consequences because of the immediate linkage between the contract code and the financial transactions. The direct handling of the assets means that the errors can be more relevant for security and have greater economic consequences than a mistake in the conventional apps. In this paper, we propose a tool for the detection of vulnerabilities in high-level languages based on automatized static analysis.
With advancement in computer technology, financial technology has also evolved. Blockchain technology has evolved over the past decade; however, this has also resulted in some malicious attacks. To earn mining rewards of a blockchain, a new type of attack, called cryptojacking, has emerged in the online world. This attack uses the computer resources of a victim to obtain mining rewards without user confirmation. However, this monetization model was originally intended to replace advertising as a source of revenue for a website. To resolve such problems, a storage verification architecture based on smart contracts was proposed in the study. The decentralized system of blockchain enables users to identify and participate in verification of mining sites through Ethereum smart contracts.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users' money (i.e., ETH or gas). In particular, we propose and develop a novel tool named GLASER, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that GLASER can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. GLASER can help stop further economic loss caused by these detected accounts. Moreover, GLASER characterizes the attacks/behaviors related to detected erasable accounts through graph analysis.
Blockchain technology has been an essential aspect for research due to its, peer to peer nature, distributed nature, and decentralization, some of its applications are Supply chain, Healthcare System, Real state, etc. Ethereum is a distributed public blockchain network that focuses on running the code of any decentralized application. A smart contract is a computer program having self-verifying, self-executing, tamper-resistant properties. Initial Coin Offering (ICO) is a process similar to crowdfunding, in which companies raise funds from investors, who, in exchange receive tokens or digital assets. This paper deals by explaining the proposed model, different functions of ERC20 standard, the different tasks performed by each function and implementing various functionalities of ERC20 Token in Solidity.
As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use of too many Bitcoin addresses, low communication efficiency, and high costs. The present research improved on this method, designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information. This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used. Under the premise of ensuring the order, the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency. Thus, a more efficient and feasible method for the application of covert communication on the blockchain is proposed. In addition, this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In this article we forecast daily closing price series of Bitcoin, Litecoin and Ethereum cryptocurrencies, using data on prices and volumes of prior days. Cryptocurrencies price behaviour is still largely unexplored, presenting new opportunities for researchers and economists to highlight similarities and differences with standard financial prices. We compared our results with various benchmarks: one recent work on Bitcoin prices forecasting that follows different approaches, a well-known paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval and another, more recent paper which gives quantitative results on stock market index predictions. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms: the Simple Linear Regression (SLR) model for uni-variate series forecast using only closing prices, and the Multiple Linear Regression (MLR) model for multivariate series using both price and volume data. We used two artificial neural networks as well: Multilayer Perceptron (MLP) and Long short-term memory (LSTM). While the entire time series resulted to be indistinguishable from a random walk, the partitioning of datasets into shorter sequences, representing different price "regimes", allows to obtain precise forecast as evaluated in terms of Mean Absolute Percentage Error(MAPE) and relative Root Mean Square Error (relativeRMSE). In this case the best results are obtained using more than one previous price, thus confirming the existence of time regimes different from random walks. Our models perform well also in terms of time complexity, and provide overall results better than those obtained in the benchmark studies, improving the state-of-the-art.
Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (<i>SVM</i>), the Artificial Neural Network (<i>ANN</i>), the Naï ve Bayes (<i>NB)</i> and the Random Forest (<i>RF</i>) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the <i>F</i> statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The <i>t</i> test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the <i>RF</i> has the highest forecasting performance in the continuous dataset, the <i>NB</i> has the lowest. On the other hand, while the <i>ANN</i> has the highest and the <i>NB</i> the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.
With the emergence of decentralized cryptocurrencies such as Bitcoin, it has become very difficult for law enforcement to detect suspicious activities, identify users and obtain transaction records for criminals who utilize the pseudoanonymity provided by the cryptocurrency system. Address clustering aims to break such pseudoanonymity by linking addresses that are controlled by the same user based on the information available from the blockchain, such as transaction graphs. There are already two widely used heuristics for Bitcoin address clustering. One is based on the multiple input addresses of transactions. The other is based on one-time change addresses. By reconsidering the one-time change address-based heuristic from the perspective of address reuse, we propose a new heuristic that detects one-time change addresses by eliminating addresses that are reused later as non-change addresses. As a result, this heuristic works for transactions whose one-time change addresses cannot be identified by the previous two heuristics. The experimental results for different scales of Bitcoin transaction data show that the proposed heuristic has a 0.33% mean contribution to the ratio of address reduction in addition to the contribution of the multiple input addresses and one-time change address heuristics.
Myoungwon Oh, Sujin Ha, Jin Hyuk Yoon, Kang‐Won Lee · 6 authors
Distributed ledger technology faces scalability problems due to a long commit time despite recent successes for cryptocurrency. Small group consensus studies have improved this scalability of distributed ledgers. However, they still have problems of the consensus process itself. For example, most blockchain systems perform serialized block proposal and consensus processing, guarantee the finality with high overhead, and handle byzantine nodes inefficiently. To address these problems, we propose a consensus system, named graph learning byzantine fault tolerance (GL BFT), which offers high parallelism and low latency under Byzantine fault. To do this, we enable a parallel pipelined agreement by separating the block proposal and the consensus process. Second, we devise two techniques of merging blocks and commit learning to guarantee the finality with little overhead. Finally, we present a path learning approach which chooses optimal paths to handle Byzantine fault. The proposed GL BFT can achieve instant finality with low message overhead among a small group of nodes even if Byzantine nodes exit. Also, we evaluate its performance on an open source blockchain protocol. Experimental results show that our design reduces data traffic required by the consensus up to 30%, one transaction is finalized within a few seconds, and optimal performance is maintained.
We document ongoing research on a digital artefact comprising a settlement layer for financial contracts traded over the counter (OTC). Working with a team of stakeholders from a hosting organization we employ the design science research methodology (DSR) in the design, implementation and evaluation of a DLT based settlement layer for OTC contracts. Our findings indicate that DLT may serve towards reducing execution costs while mitigating counterparty credit risk through deterministic automation of key processes in the OTC contract lifecycle. Extrapolating these early results, we introduce three general propositions on the systemic and commercial implications of DLT. We contribute to the broader IS discourse on the capacity for emerging digital technologies to upend traditional knowledge processes in the financial services.
Object of the research: information storage, transmission and processing systems. Subject of the research: approaches to implementing the distributed ledger technology, in particular, the blockchain technology, as well as implementation issues and feasibility. Purpose of the work: enhancing the safety of data storage, transmission and processing by embedding distributed ledgers in the existing information system. Research methods: the theoretical and analytical research is supposed to underlie the study of the experience building upon the current application and possible options for boosting the potential of the distributed ledger technology, in particular, the blockchain technology. Scientific novelty. The results of the research are supposed to be used to issue proposals and recommendations on how to improve the security of data storage and processing through the use of distributed ledger technology. Practical importance. Practical perspectives consist in the fact that the discussed process of developing and implementing a distributed system simplifies further integration of the technology under consideration with the resulting blockchain service enabling data storage inside a non-modifiable registry.
Mirko Zichichi, Michele Contu, Stefano Ferretti, Victor Rodrı́guez-Doncel
Personal data has undoubtedly assumed a great value with the advancements on technologies able to gather it and infer from it. The businesses that operate in a data-driven economy offer services that rely on data collected about their users and usually they store this personal information in “silos” that impede transparency on their use and possibilities of easy interactions. The introduction in EU of the General Data Protection Regulation (GDPR) moves this economy towards a user-centered vision, in which individuals have rights for their data sovereignty and the free portability of it. However, more efforts are needed to reach both transparency and balance between privacy and data sharing. In this paper, we present a solution to promote the development of personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs) and a Sensing-as-a-Service (SaaS) model, in order to enhance the privacy of individuals, following the principles of personal data sovereignty and interoperability. Moreover, we provide experimental results of an implementation based on IOTA, a promising DLT for managing and transacting IoT data
This paper is a review of literature, resources and advances in peer to peer ledger technology adoption and not just the evolution of ledger technology in power sector. It also introduces the concept of transition architectures from the field of enterprise architecture to fill the gap in adoption. It reviews the possibility of de-centralising energy optimally and the possibility of using an architecture roadmap of milestones (transition states) via transition architectures to enhance adoption rates. The paper evaluates features of distributed ledgers. It identifies the core features that can be introduced independent of each other, thereby making a minimal usable product in each transition state via transition architectures with a view of target state that would encompass all core features. It covers engaging the current participants (generators, transmission, distributors, metering, retailers, prosumers, banks, regulators and others); and how a gradual evolving nature via transition architectures would increase adoption due to smooth transition as the roles of current participants evolve while the market dynamics re-orchestrates. A discussion and proposal for a custom distributed ledger framework for energy domain balancing decentralisation, immutability, verification and transparency in a gradual manner; to address privacy, compliance, regulations and peer to peer trading is presented.
Jan 1, 2020·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Distributed ledger technology (DLT) appears to be one of the most promising technologies in the field of supply chain management (SCM). However, as the technology is still evolving, only limited empirical evidence has been analyzed, managers and scientific scholars alike seek to understand how DLT can help improving SCM. This study aims to shed light into the current DLT applications in SCM to identify the foundation of the technology for SCM and uncover what DLT brings to the table. It develops seven foundational characteristics of DLT in SCM that describe both the nature of DLT and its characteristics for SCM. The study reveals that DLTs are interorganizational information systems that are diverse in their realizations and enable modular platform ecosystems. Nowadays application in SCM build on steady data availability, selective transparency, high authenticity and a source of mutual trust.
In recent years, various food-safety issues have aroused public concern regarding safety in the food supply chain. Since grains are closely linked to human life and health, it is necessary to effectively manage information in the grain supply chain. The grain supply chain is characterized by a long life cycle, complex links, various hazards, and heterogeneous information sources. Problems with traditional traceability systems include easy data tampering, difficult hazardous-material information management, the “information isolated island” problem, and low traceability efficiency in the whole supply chain. Blockchain is a distributed computing paradigm characterized by decentralization, network-wide recording, security, and reliability. As such, it can reduce administrative costs and improve the efficiency of information management. Based on literature research and a field investigation of wheat-processing enterprises in Shandong Province, We analyze the operation process of grain supply chain. This study, therefore, proposed a new system architecture in the entire grain supply chain based on blockchain technology and designed a multimode storage mechanism that combines chain storage. This prototype system was tested and verified using actual cases and application scenarios. Compared to traditional systems, the proposed system is characterized by data security and reliability, information interconnection and intercommunication, real-time sharing of hazardous-material information, and dynamic and credible whole-process tracing. As such, this system is highly significant and has reference value for guaranteeing food quality and safety-process traceability.